Data have always been the lifeblood of the sciences, giving rise to new theories, invalidating old.
Data are costly to acquire and in most fields have long been a constraint on progress. Over the last few decades this situation has structurally changed in several fields, with information technology leading to rapidly declining costs of data acquisition.
Since more data are better than fewer, ceteris paribus on quality, growing data has been welcomed. But in the last few years, in multiple fields, there have appeared so many data that no extant theories are able to explain more than a tiny fraction of such data.
In this talk Prof Axtell will illustrate this phenomenon based on quasi-comprehensive micro-data with respect to American business firms. There are a large number of theories of firms, none of which appear capable of rationalising these data. Prof Axtell will discuss the use of machine learning approaches for modeling data of this type, the value of explicable models, and how machine learning techniques can be used to create new data structures in order to better understand the data. Finally, he will mention complications that arise in the social sciences when individuals use machine learning to make sense of their environment.
This is a joint event with INET Oxford and the Oxford Martin Programme on the Post-Carbon Transition